Optimizing Molecular Models Through Force-Field Parameterization via the Efficient Combination of Modular Program Packages

Hülsmann, Marco; Krämer, Andreas; Heinrich, Doron D.; Krämer-Fuhrmann, Ottmar; Reith, Dirk

doi:10.1007/978-981-10-1128-3_4

Cited by 12 publications

(7 citation statements)

References 84 publications

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“…In this context, MR acts as a physics-based metamodel ; the term metamodel, or “response surface model”, designates a function that can be used to estimate the outcome of a simulation without running one. Such metamodels are capable of effectively reducing the number of simulations in automated FF optimization. ,, In contrast to typical models used in machine learning, such as the neural networks in ref and , the MR metamodel incorporates the physics from each snapshot into its predictions.…”

Section: Optimization Of Additive Modelsmentioning

confidence: 99%

Interactions of Water and Alkanes: Modifying Additive Force Fields to Account for Polarization Effects

Krämer

Pickard

Huang

et al. 2019

J. Chem. Theory Comput.

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Atomistic biomolecular simulations predominantly utilize additive force fields (FF), where the electrostatic potential is modeled by fixed point charges. Among other consequences, the lack of polarizability in these models undermines the balance of hydrophilic/hydrophobic non-bonded interactions. Simulations of water/alkane systems using the TIP3P water model and CHARMM36 parameters reveal a 1 kcal/mol overestimate of the experimental transfer free energy of water to hexadecane; more recent optimized water models (SPC/E, TIP4P/2005, TIP4P-Ew, TIP3P-FB, TIP4P-FB, OPC, TIP4P-D) overestimate this transfer free energy by approximately 2 kcal/mol. In contrast, the polarizable SWM4-NDP and SWM6 water models reproduce experimental values to within statistical error. As an alternative to explicitly modeling polarizability, this paper develops an efficient automated workflow to optimize pair-specific Lennard-Jones parameters within an additive FF. Water/hexadecane is used as a prototype and the free energy of water transfer to hexadecane as a target. The optimized model yields quantitative agreement with the experimental transfer free energy and improves the water/hexadecane interfacial tension by 6%. Simulations of five different lipid bilayers show a strong increase of water permeabilities compared to the unmodified CHARMM36 lipid FF which consistently improves match with experiment: the orderof-magnitude underestimate for monounsaturated bilayers is rectified and the factor of 2.8-4 underestimate for saturated bilayers is turned into a factor of1.5-3 overestimate. While agreement with experiment is decreased for the diffusion constant of water in hexadecane, alkane transfer free energies, and the bilayers' area per lipid, the method provides a permeant-specific

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Section: Optimization Of Additive Modelsmentioning

confidence: 99%

Interactions of Water and Alkanes: Modifying Additive Force Fields to Account for Polarization Effects

Krämer

Pickard

Huang

et al. 2019

J. Chem. Theory Comput.

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“…The parameterization workflow is usually accomplished using scripts to glue the required components together; a relatively early example is a tcsh script for simplex optimization by Faller and coworkers. 129 More recently, several parameterization programs have been made available for further generality and reproducibility; these include ForceBalance (developed by one of us), 31,53 potfit by Brommer et al, 130 and Wolf(2)Pack by Hulsmann et al 131 We also note related research in the AMOEBA, AMBER, and CHARMM simulation communities that provide automated programs for parameterizing new molecules by following fixed procedures; these methods are not directly applicable to water or developing novel functional forms.…”

Section: Future Water Models Based On Guidance From Edamentioning

confidence: 99%

Advanced models for water simulations

Demerdash

Wang

Head‐Gordon

2017

WIREs Comput Mol Sci

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Molecular simulations of water using classical, molecular mechanic potential energy functions have enjoyed a 50-year history of development, and much has been learned regarding their parameterization and the essential physics that must be captured in order to reproduce water properties across the phase diagram and across system sizes, from the dimer to the condensed phase. While pairwise-additive force fields using fixed, point charge-based electrostatics have dominated this history owing to computational cost, their limitations in transferability are being recognized, owing particularly to the lack of many-body effects, as well as an inherent difficulty in capturing quantum mechanical effects that become important at short intermolecular separation. This has spurred an impressive development of novel functional forms and parameterization schemes to account for such effects, especially the leading many-body effect of polarization. This review discusses recent efforts in the development of advanced models of water, particularly with regard to important details of their parameterization from quantum mechanical or experimental data, the development of novel functional forms including machine learning-based models, and algorithms that reduce the computational cost of polarization dramatically, permitting them to potentially become competitive with pairwise-additive models as the standby of condensed-phase simulation. These technical developments are appraised based on their ability to impact numerical calculations on water, particularly the condensed phase, and it is hoped that this article provides a clear connection between the essential physics captured by the model and their fitness across a range of environments.

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“…25,43,44 This error is acceptable in most circumstances but complicates optimization with noisy parameter sensitivities computed through finite differences. 45,46 Furthermore, simply obtaining the surface area within the experimental error (which can be larger than 1%) does not ensure the accuracy of other properties. Finally, lipid FFs are frequently subjected to adjustments to study new experimental results and utilize simulation approaches, so efficient parametrization methods are essential.…”

Section: Introductionmentioning

confidence: 99%

“…However, the atomistic simulation methods still suffer from the fact that the range of time and length scales by which different processes occur in membranes is vast . For a simple calculation of bilayer surface area, the uncertainty from a simulation on the 100 ns time scale is typically less than 1%. ,, This error is acceptable in most circumstances but complicates optimization with noisy parameter sensitivities computed through finite differences. , Furthermore, simply obtaining the surface area within the experimental error (which can be larger than 1%) does not ensure the accuracy of other properties. Finally, lipid FFs are frequently subjected to adjustments to study new experimental results and utilize simulation approaches, so efficient parametrization methods are essential.…”

Section: Introductionmentioning

confidence: 99%

Semi-automated Optimization of the CHARMM36 Lipid Force Field to Include Explicit Treatment of Long-Range Dispersion

Krämer

Venable

et al. 2021

J. Chem. Theory Comput.

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The development of the CHARMM lipid force field (FF) can be traced back to the early 1990s with its current version denoted CHARMM36 (C36). The parametrization of C36 utilized high-level quantum mechanical data and free energy calculations of model compounds before parameters were manually adjusted to yield agreement with experimental properties of lipid bilayers. While such manual fine-tuning of FF parameters is based on intuition and trial-and-error, automated methods can identify beneficial modifications of the parameters via their sensitivities and thereby guide the optimization process. This work introduces a semi-automated approach to reparametrize the CHARMM lipid FF with consistent inclusion of long-range dispersion through the Lennard-Jones particle-mesh Ewald (LJ-PME) approach. The optimization method is based on thermodynamic reweighting with regularization with respect to the C36 set. Two independent optimizations with different topology restrictions are presented. Targets of the optimizations are primarily liquid crystalline phase properties of lipid bilayers and the compression isotherm of monolayers. Pair correlation functions between water and lipid functional groups in aqueous solution are also included to address headgroup hydration. While the physics of the reweighting strategy itself is well-understood, applying it to heterogeneous, complex anisotropic systems poses additional challenges. These were overcome through careful selection of target properties and reweighting settings allowing for the successful incorporation of the explicit treatment of long-range dispersion, and we denote the newly optimized lipid force field as C36/LJ-PME. The current implementation of the optimization protocol will facilitate the future development of the CHARMM and related lipid force fields.

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Optimizing Molecular Models Through Force-Field Parameterization via the Efficient Combination of Modular Program Packages

Cited by 12 publications

References 84 publications

Interactions of Water and Alkanes: Modifying Additive Force Fields to Account for Polarization Effects

Interactions of Water and Alkanes: Modifying Additive Force Fields to Account for Polarization Effects

Advanced models for water simulations

Semi-automated Optimization of the CHARMM36 Lipid Force Field to Include Explicit Treatment of Long-Range Dispersion

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